Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan.

Due to its diverse topography, Pakistan faces different types of floods each year, which cause substantial physical, environmental, and socioeconomic damage. However, the susceptibility of specific regions to different flood types remains unexplored. To the best of our knowledge for the first time, this study employed an integrated approach by leveraging a GIS-based Analytical Hierarchy Process (AHP), remote sensing, and machine learning (ML) algorithms, to assess susceptibility to three different types of flooding in Peshawar, Pakistan.

Systematic comparison of machine learning algorithms to develop and validate predictive models for periodontitis.

Aim: The aim of this study was to compare the validity of different machine learning algorithms to develop and validate predictive models for periodontitis.

Materials And Methods: Using national survey data from Taiwan (n = 3453) and the United States (n = 3685), predictors of periodontitis were extracted from the datasets and pre-processed, and then 10 machine learning algorithms were trained to develop predictive models. The models were validated both internally (bootstrap sampling) and externally (alternative country's dataset).

Tracking a Decentralized Linear Trajectory in an Intermittent Observation Environment.

Faults and failures are familiar case studies in centralized and decentralized tracking systems. The processing of sensor data becomes more severe in the presence of faults/failures and/or noise. Effective schemes have been presented for decentralized systems, in the presence of faults only.